The determination of an accurate image-to-physical space registration is a fundamental step in providing meaningful guidance information to surgeons via image-guided surgery (IGS). A significant body of research has been dedicated to the use of IGS techniques for neurosurgical applications and has resulted in several commercially available systems. A common feature of the IGS technology for neurosurgery is the use of point-based landmarks, via bone-implanted or skin-affixed fiducial markers, to provide the registration of image and physical space. The use of such point-based techniques is greatly facilitated in neurosurgical IGS by the rigid anatomy surrounding the tissues of interest (e.g., the skull). Unfortunately, the use of such point-based techniques is not applicable for open abdominal IGS due to the lack of rigid anatomical landmarks and the inability to preoperatively attach fiducial markers that will remain in a fixed position during the IGS procedure.
Since the use of rigid, point-based landmarks is not feasible for open abdominal IGS, surface-based techniques have been proposed to determine the registration between the preoperative images and the intraoperative presentation. For example, the iterative closest point (ICP) algorithm has traditionally been used to determine the transformation between the image-space surface of an organ and/or other soft tissues of interest. In ICP methods, the transformation is generally derived from preoperative image segmentations, and the intraoperative tissue surfaces. Intraoperative data for use in abdominal IGS is typically acquired using an optically tracked probe, a laser range scanner (LRS), or intraoperative ultrasound (iUS), and other methods.
The typical protocol for surface-based image-to-physical space registration in abdominal IGS begins with the selection of anatomical fiducial points in the preoperative image sets prior to surgery. The homologous physical-space location of these anatomical fiducials is then digitized during the surgical procedure such that a point-based initial alignment registration can be performed. The point-based registration serves to provide a reasonable initial pose for the ICP algorithm, which is used to register the tissue surfaces derived from preoperative images and the intraoperative data.
However, the surface alignment provided by the ICP algorithm is highly dependent on the initial pose of the tissue surfaces. Therefore, gross errors in the initial alignment provided by the point-based registration can result in erroneous surface alignments. While initial pose is important, another aspect of misalignment that can confound the ICP algorithm is the presence of intraoperative deformation. That is when organ and other soft tissues are surgically presented intraoperatively for surface acquisition (such as by laser range scanning), the soft tissues have generally undergone deformation due to routine surgical manipulation. Errors associated with pose or deformation introduced into any form of rigid registration will generally compromise the guidance information relayed to the surgeon. Some examples of soft tissue deformation due to surgical manipulation are (1) gravity-induced deformations of the liver due to reorientation of the organ with respect to the direction of gravity in the open-abdomen, (2) the effects of tissue mobilization and organ packing, and (3) changes in organ perfusion.